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<div class="title">Extended Image Processing</div>  </div>
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Modules</h2></td></tr>
<tr class="memitem:de/d51/group__ximgproc__edge"><td align="right" class="memItemLeft" valign="top"> </td><td class="memItemRight" valign="bottom"><a class="el" href="../../de/d51/group__ximgproc__edge.html">Structured forests for fast edge detection</a></td></tr>
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Functions</h2></td></tr>
<tr class="memitem:gaffedd976e0a8efb5938107acab185ec2"><td align="right" class="memItemLeft" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="../../df/d2d/group__ximgproc.html#gaffedd976e0a8efb5938107acab185ec2">cv::ximgproc::anisotropicDiffusion</a> (<a class="el" href="../../dc/d84/group__core__basic.html#ga353a9de602fe76c709e12074a6f362ba">InputArray</a> src, <a class="el" href="../../dc/d84/group__core__basic.html#gaad17fda1d0f0d1ee069aebb1df2913c0">OutputArray</a> dst, float alpha, float K, int niters)</td></tr>
<tr class="memdesc:gaffedd976e0a8efb5938107acab185ec2"><td class="mdescLeft"> </td><td class="mdescRight">Performs anisotropic diffusion on an image.  <a href="../../df/d2d/group__ximgproc.html#gaffedd976e0a8efb5938107acab185ec2">More...</a><br/></td></tr>
<tr class="separator:gaffedd976e0a8efb5938107acab185ec2"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:ga86fcda65ced0aafa2741088d82e9161c"><td align="right" class="memItemLeft" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="../../df/d2d/group__ximgproc.html#ga86fcda65ced0aafa2741088d82e9161c">cv::ximgproc::edgePreservingFilter</a> (<a class="el" href="../../dc/d84/group__core__basic.html#ga353a9de602fe76c709e12074a6f362ba">InputArray</a> src, <a class="el" href="../../dc/d84/group__core__basic.html#gaad17fda1d0f0d1ee069aebb1df2913c0">OutputArray</a> dst, int d, double <a class="el" href="../../d7/d1b/group__imgproc__misc.html#gae8a4a146d1ca78c626a53577199e9c57">threshold</a>)</td></tr>
<tr class="memdesc:ga86fcda65ced0aafa2741088d82e9161c"><td class="mdescLeft"> </td><td class="mdescRight">Smoothes an image using the Edge-Preserving filter.  <a href="../../df/d2d/group__ximgproc.html#ga86fcda65ced0aafa2741088d82e9161c">More...</a><br/></td></tr>
<tr class="separator:ga86fcda65ced0aafa2741088d82e9161c"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:gab042a5032bbb85275f1fd3e04e7c7660"><td align="right" class="memItemLeft" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="../../df/d2d/group__ximgproc.html#gab042a5032bbb85275f1fd3e04e7c7660">cv::ximgproc::niBlackThreshold</a> (<a class="el" href="../../dc/d84/group__core__basic.html#ga353a9de602fe76c709e12074a6f362ba">InputArray</a> _src, <a class="el" href="../../dc/d84/group__core__basic.html#gaad17fda1d0f0d1ee069aebb1df2913c0">OutputArray</a> _dst, double maxValue, int type, int blockSize, double k, int binarizationMethod=<a class="el" href="../../d9/d29/namespacecv_1_1ximgproc.html#a77765f3d60539a658097c1dd8faedf24a46d86176b25f88d84c21eda2a9fdd6f8">BINARIZATION_NIBLACK</a>, double r=128)</td></tr>
<tr class="memdesc:gab042a5032bbb85275f1fd3e04e7c7660"><td class="mdescLeft"> </td><td class="mdescRight">Performs thresholding on input images using Niblack's technique or some of the popular variations it inspired.  <a href="../../df/d2d/group__ximgproc.html#gab042a5032bbb85275f1fd3e04e7c7660">More...</a><br/></td></tr>
<tr class="separator:gab042a5032bbb85275f1fd3e04e7c7660"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:ga50d064b92f63916f4162474eea22d656"><td align="right" class="memItemLeft" valign="top"><a class="el" href="../../dc/d84/group__core__basic.html#ga392a6836e5dbc164888f4e39c7d9d9af">Matx23d</a> </td><td class="memItemRight" valign="bottom"><a class="el" href="../../df/d2d/group__ximgproc.html#ga50d064b92f63916f4162474eea22d656">cv::ximgproc::PeiLinNormalization</a> (<a class="el" href="../../dc/d84/group__core__basic.html#ga353a9de602fe76c709e12074a6f362ba">InputArray</a> I)</td></tr>
<tr class="memdesc:ga50d064b92f63916f4162474eea22d656"><td class="mdescLeft"> </td><td class="mdescRight">Calculates an affine transformation that normalize given image using Pei&amp;Lin Normalization.  <a href="../../df/d2d/group__ximgproc.html#ga50d064b92f63916f4162474eea22d656">More...</a><br/></td></tr>
<tr class="separator:ga50d064b92f63916f4162474eea22d656"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:gaeef3f93a672fa5e3e20adbf6e5e32986"><td align="right" class="memItemLeft" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="../../df/d2d/group__ximgproc.html#gaeef3f93a672fa5e3e20adbf6e5e32986">cv::ximgproc::PeiLinNormalization</a> (<a class="el" href="../../dc/d84/group__core__basic.html#ga353a9de602fe76c709e12074a6f362ba">InputArray</a> I, <a class="el" href="../../dc/d84/group__core__basic.html#gaad17fda1d0f0d1ee069aebb1df2913c0">OutputArray</a> T)</td></tr>
<tr class="separator:gaeef3f93a672fa5e3e20adbf6e5e32986"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:ga37002c6ca80c978edb6ead5d6b39740c"><td align="right" class="memItemLeft" valign="top">void </td><td class="memItemRight" valign="bottom"><a class="el" href="../../df/d2d/group__ximgproc.html#ga37002c6ca80c978edb6ead5d6b39740c">cv::ximgproc::thinning</a> (<a class="el" href="../../dc/d84/group__core__basic.html#ga353a9de602fe76c709e12074a6f362ba">InputArray</a> src, <a class="el" href="../../dc/d84/group__core__basic.html#gaad17fda1d0f0d1ee069aebb1df2913c0">OutputArray</a> dst, int thinningType=<a class="el" href="../../d9/d29/namespacecv_1_1ximgproc.html#aa244a73deb4e58ae70ee96afe9d2460bacbe8f65466c9d11a295d1bc7413e3404">THINNING_ZHANGSUEN</a>)</td></tr>
<tr class="memdesc:ga37002c6ca80c978edb6ead5d6b39740c"><td class="mdescLeft"> </td><td class="mdescRight">Applies a binary blob thinning operation, to achieve a skeletization of the input image.  <a href="../../df/d2d/group__ximgproc.html#ga37002c6ca80c978edb6ead5d6b39740c">More...</a><br/></td></tr>
<tr class="separator:ga37002c6ca80c978edb6ead5d6b39740c"><td class="memSeparator" colspan="2"> </td></tr>
</table>
<a id="details" name="details"></a><h2 class="groupheader">Detailed Description</h2>
<h2 class="groupheader">Function Documentation</h2>
<a id="gaffedd976e0a8efb5938107acab185ec2"></a>
<h2 class="memtitle"><span class="permalink"><a href="#gaffedd976e0a8efb5938107acab185ec2">◆ </a></span>anisotropicDiffusion()</h2>
<div class="memitem">
<div class="memproto">
      <table class="memname">
        <tr>
          <td class="memname">void cv::ximgproc::anisotropicDiffusion </td>
          <td>(</td>
          <td class="paramtype"><a class="el" href="../../dc/d84/group__core__basic.html#ga353a9de602fe76c709e12074a6f362ba">InputArray</a> </td>
          <td class="paramname"><em>src</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype"><a class="el" href="../../dc/d84/group__core__basic.html#gaad17fda1d0f0d1ee069aebb1df2913c0">OutputArray</a> </td>
          <td class="paramname"><em>dst</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">float </td>
          <td class="paramname"><em>alpha</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">float </td>
          <td class="paramname"><em>K</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">int </td>
          <td class="paramname"><em>niters</em> </td>
        </tr>
        <tr>
          <td></td>
          <td>)</td>
          <td></td><td></td>
        </tr>
      </table><table class="python_language"><tr><th colspan="999" style="text-align:left">Python:</th></tr><tr><td style="width: 20px;"></td><td>dst</td><td>=</td><td>cv.ximgproc.anisotropicDiffusion(</td><td class="paramname">src, alpha, K, niters[, dst]</td><td>)</td></tr></table>
</div><div class="memdoc">
<p><code>#include &lt;<a class="el" href="../../df/d6c/ximgproc_8hpp.html">opencv2/ximgproc.hpp</a>&gt;</code></p>
<p>Performs anisotropic diffusion on an image. </p>
<p>The function applies Perona-Malik anisotropic diffusion to an image. This is the solution to the partial differential equation:</p>
<p class="formulaDsp">
\[{\frac {\partial I}{\partial t}}={\mathrm {div}}\left(c(x,y,t)\nabla I\right)=\nabla c\cdot \nabla I+c(x,y,t)\Delta I\]
</p>
<p>Suggested functions for c(x,y,t) are:</p>
<p class="formulaDsp">
\[c\left(\|\nabla I\|\right)=e^{{-\left(\|\nabla I\|/K\right)^{2}}}\]
</p>
<p>or</p>
<p class="formulaDsp">
\[ c\left(\|\nabla I\|\right)={\frac {1}{1+\left({\frac {\|\nabla I\|}{K}}\right)^{2}}} \]
</p>
<dl class="params"><dt>Parameters</dt><dd>
  <table class="params">
    <tr><td class="paramname">src</td><td>Source image with 3 channels. </td></tr>
    <tr><td class="paramname">dst</td><td>Destination image of the same size and the same number of channels as src . </td></tr>
    <tr><td class="paramname">alpha</td><td>The amount of time to step forward by on each iteration (normally, it's between 0 and 1). </td></tr>
    <tr><td class="paramname">K</td><td>sensitivity to the edges </td></tr>
    <tr><td class="paramname">niters</td><td>The number of iterations </td></tr>
  </table>
  </dd>
</dl>
</div>
</div>
<a id="ga86fcda65ced0aafa2741088d82e9161c"></a>
<h2 class="memtitle"><span class="permalink"><a href="#ga86fcda65ced0aafa2741088d82e9161c">◆ </a></span>edgePreservingFilter()</h2>
<div class="memitem">
<div class="memproto">
      <table class="memname">
        <tr>
          <td class="memname">void cv::ximgproc::edgePreservingFilter </td>
          <td>(</td>
          <td class="paramtype"><a class="el" href="../../dc/d84/group__core__basic.html#ga353a9de602fe76c709e12074a6f362ba">InputArray</a> </td>
          <td class="paramname"><em>src</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype"><a class="el" href="../../dc/d84/group__core__basic.html#gaad17fda1d0f0d1ee069aebb1df2913c0">OutputArray</a> </td>
          <td class="paramname"><em>dst</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">int </td>
          <td class="paramname"><em>d</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">double </td>
          <td class="paramname"><em>threshold</em> </td>
        </tr>
        <tr>
          <td></td>
          <td>)</td>
          <td></td><td></td>
        </tr>
      </table><table class="python_language"><tr><th colspan="999" style="text-align:left">Python:</th></tr><tr><td style="width: 20px;"></td><td>dst</td><td>=</td><td>cv.ximgproc.edgePreservingFilter(</td><td class="paramname">src, d, threshold[, dst]</td><td>)</td></tr></table>
</div><div class="memdoc">
<p><code>#include &lt;<a class="el" href="../../d0/d5b/edgepreserving__filter_8hpp.html">opencv2/ximgproc/edgepreserving_filter.hpp</a>&gt;</code></p>
<p>Smoothes an image using the Edge-Preserving filter. </p>
<p>The function smoothes Gaussian noise as well as salt &amp; pepper noise. For more details about this implementation, please see [ReiWoe18] Reich, S. and Wörgötter, F. and Dellen, B. (2018). A Real-Time Edge-Preserving Denoising Filter. Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP): Visapp, 85-94, 4. DOI: 10.5220/0006509000850094.</p>
<dl class="params"><dt>Parameters</dt><dd>
  <table class="params">
    <tr><td class="paramname">src</td><td>Source 8-bit 3-channel image. </td></tr>
    <tr><td class="paramname">dst</td><td>Destination image of the same size and type as src. </td></tr>
    <tr><td class="paramname">d</td><td>Diameter of each pixel neighborhood that is used during filtering. Must be greater or equal 3. </td></tr>
    <tr><td class="paramname">threshold</td><td>Threshold, which distinguishes between noise, outliers, and data. </td></tr>
  </table>
  </dd>
</dl>
</div>
</div>
<a id="gab042a5032bbb85275f1fd3e04e7c7660"></a>
<h2 class="memtitle"><span class="permalink"><a href="#gab042a5032bbb85275f1fd3e04e7c7660">◆ </a></span>niBlackThreshold()</h2>
<div class="memitem">
<div class="memproto">
      <table class="memname">
        <tr>
          <td class="memname">void cv::ximgproc::niBlackThreshold </td>
          <td>(</td>
          <td class="paramtype"><a class="el" href="../../dc/d84/group__core__basic.html#ga353a9de602fe76c709e12074a6f362ba">InputArray</a> </td>
          <td class="paramname"><em>_src</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype"><a class="el" href="../../dc/d84/group__core__basic.html#gaad17fda1d0f0d1ee069aebb1df2913c0">OutputArray</a> </td>
          <td class="paramname"><em>_dst</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">double </td>
          <td class="paramname"><em>maxValue</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">int </td>
          <td class="paramname"><em>type</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">int </td>
          <td class="paramname"><em>blockSize</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">double </td>
          <td class="paramname"><em>k</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">int </td>
          <td class="paramname"><em>binarizationMethod</em> = <code><a class="el" href="../../d9/d29/namespacecv_1_1ximgproc.html#a77765f3d60539a658097c1dd8faedf24a46d86176b25f88d84c21eda2a9fdd6f8">BINARIZATION_NIBLACK</a></code>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">double </td>
          <td class="paramname"><em>r</em> = <code>128</code> </td>
        </tr>
        <tr>
          <td></td>
          <td>)</td>
          <td></td><td></td>
        </tr>
      </table><table class="python_language"><tr><th colspan="999" style="text-align:left">Python:</th></tr><tr><td style="width: 20px;"></td><td>_dst</td><td>=</td><td>cv.ximgproc.niBlackThreshold(</td><td class="paramname">_src, maxValue, type, blockSize, k[, _dst[, binarizationMethod[, r]]]</td><td>)</td></tr></table>
</div><div class="memdoc">
<p><code>#include &lt;<a class="el" href="../../df/d6c/ximgproc_8hpp.html">opencv2/ximgproc.hpp</a>&gt;</code></p>
<p>Performs thresholding on input images using Niblack's technique or some of the popular variations it inspired. </p>
<p>The function transforms a grayscale image to a binary image according to the formulae:</p><ul>
<li><b>THRESH_BINARY</b> <p class="formulaDsp">
\[dst(x,y) = \fork{\texttt{maxValue}}{if \(src(x,y) &gt; T(x,y)\)}{0}{otherwise}\]
</p>
</li>
<li><b>THRESH_BINARY_INV</b> <p class="formulaDsp">
\[dst(x,y) = \fork{0}{if \(src(x,y) &gt; T(x,y)\)}{\texttt{maxValue}}{otherwise}\]
</p>
 where \(T(x,y)\) is a threshold calculated individually for each pixel.</li>
</ul>
<p>The threshold value \(T(x, y)\) is determined based on the binarization method chosen. For classic Niblack, it is the mean minus \( k \) times standard deviation of \(\texttt{blockSize} \times\texttt{blockSize}\) neighborhood of \((x, y)\).</p>
<p>The function can't process the image in-place.</p>
<dl class="params"><dt>Parameters</dt><dd>
  <table class="params">
    <tr><td class="paramname">_src</td><td>Source 8-bit single-channel image. </td></tr>
    <tr><td class="paramname">_dst</td><td>Destination image of the same size and the same type as src. </td></tr>
    <tr><td class="paramname">maxValue</td><td>Non-zero value assigned to the pixels for which the condition is satisfied, used with the THRESH_BINARY and THRESH_BINARY_INV thresholding types. </td></tr>
    <tr><td class="paramname">type</td><td>Thresholding type, see <a class="el" href="../../d7/d1b/group__imgproc__misc.html#gaa9e58d2860d4afa658ef70a9b1115576">cv::ThresholdTypes</a>. </td></tr>
    <tr><td class="paramname">blockSize</td><td>Size of a pixel neighborhood that is used to calculate a threshold value for the pixel: 3, 5, 7, and so on. </td></tr>
    <tr><td class="paramname">k</td><td>The user-adjustable parameter used by Niblack and inspired techniques. For Niblack, this is normally a value between 0 and 1 that is multiplied with the standard deviation and subtracted from the mean. </td></tr>
    <tr><td class="paramname">binarizationMethod</td><td>Binarization method to use. By default, Niblack's technique is used. Other techniques can be specified, see <a class="el" href="../../d9/d29/namespacecv_1_1ximgproc.html#a77765f3d60539a658097c1dd8faedf24" title="Specifies the binarization method to use in cv::ximgproc::niBlackThreshold. ">cv::ximgproc::LocalBinarizationMethods</a>. </td></tr>
    <tr><td class="paramname">r</td><td>The user-adjustable parameter used by Sauvola's technique. This is the dynamic range of standard deviation. </td></tr>
  </table>
  </dd>
</dl>
<dl class="section see"><dt>See also</dt><dd><a class="el" href="../../d7/d1b/group__imgproc__misc.html#gae8a4a146d1ca78c626a53577199e9c57" title="Applies a fixed-level threshold to each array element. ">threshold</a>, <a class="el" href="../../d7/d1b/group__imgproc__misc.html#ga72b913f352e4a1b1b397736707afcde3" title="Applies an adaptive threshold to an array. ">adaptiveThreshold</a> </dd></dl>
</div>
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<h2 class="memtitle"><span class="permalink"><a href="#ga50d064b92f63916f4162474eea22d656">◆ </a></span>PeiLinNormalization() <span class="overload">[1/2]</span></h2>
<div class="memitem">
<div class="memproto">
      <table class="memname">
        <tr>
          <td class="memname"><a class="el" href="../../dc/d84/group__core__basic.html#ga392a6836e5dbc164888f4e39c7d9d9af">Matx23d</a> cv::ximgproc::PeiLinNormalization </td>
          <td>(</td>
          <td class="paramtype"><a class="el" href="../../dc/d84/group__core__basic.html#ga353a9de602fe76c709e12074a6f362ba">InputArray</a> </td>
          <td class="paramname"><em>I</em></td><td>)</td>
          <td></td>
        </tr>
      </table><table class="python_language"><tr><th colspan="999" style="text-align:left">Python:</th></tr><tr><td style="width: 20px;"></td><td>T</td><td>=</td><td>cv.ximgproc.PeiLinNormalization(</td><td class="paramname">I[, T]</td><td>)</td></tr></table>
</div><div class="memdoc">
<p><code>#include &lt;<a class="el" href="../../dd/db0/peilin_8hpp.html">opencv2/ximgproc/peilin.hpp</a>&gt;</code></p>
<p>Calculates an affine transformation that normalize given image using Pei&amp;Lin Normalization. </p>
<p>Assume given image \(I=T(\bar{I})\) where \(\bar{I}\) is a normalized image and \(T\) is an affine transformation distorting this image by translation, rotation, scaling and skew. The function returns an affine transformation matrix corresponding to the transformation \(T^{-1}\) described in [PeiLin95]. For more details about this implementation, please see [PeiLin95] Soo-Chang Pei and Chao-Nan Lin. Image normalization for pattern recognition. Image and Vision Computing, Vol. 13, N.10, pp. 711-723, 1995.</p>
<dl class="params"><dt>Parameters</dt><dd>
  <table class="params">
    <tr><td class="paramname">I</td><td>Given transformed image. </td></tr>
  </table>
  </dd>
</dl>
<dl class="section return"><dt>Returns</dt><dd>Transformation matrix corresponding to inversed image transformation </dd></dl>
</div>
</div>
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<h2 class="memtitle"><span class="permalink"><a href="#gaeef3f93a672fa5e3e20adbf6e5e32986">◆ </a></span>PeiLinNormalization() <span class="overload">[2/2]</span></h2>
<div class="memitem">
<div class="memproto">
      <table class="memname">
        <tr>
          <td class="memname">void cv::ximgproc::PeiLinNormalization </td>
          <td>(</td>
          <td class="paramtype"><a class="el" href="../../dc/d84/group__core__basic.html#ga353a9de602fe76c709e12074a6f362ba">InputArray</a> </td>
          <td class="paramname"><em>I</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype"><a class="el" href="../../dc/d84/group__core__basic.html#gaad17fda1d0f0d1ee069aebb1df2913c0">OutputArray</a> </td>
          <td class="paramname"><em>T</em> </td>
        </tr>
        <tr>
          <td></td>
          <td>)</td>
          <td></td><td></td>
        </tr>
      </table><table class="python_language"><tr><th colspan="999" style="text-align:left">Python:</th></tr><tr><td style="width: 20px;"></td><td>T</td><td>=</td><td>cv.ximgproc.PeiLinNormalization(</td><td class="paramname">I[, T]</td><td>)</td></tr></table>
</div><div class="memdoc">
<p><code>#include &lt;<a class="el" href="../../dd/db0/peilin_8hpp.html">opencv2/ximgproc/peilin.hpp</a>&gt;</code></p>
<p>This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts. </p>
</div>
</div>
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<h2 class="memtitle"><span class="permalink"><a href="#ga37002c6ca80c978edb6ead5d6b39740c">◆ </a></span>thinning()</h2>
<div class="memitem">
<div class="memproto">
      <table class="memname">
        <tr>
          <td class="memname">void cv::ximgproc::thinning </td>
          <td>(</td>
          <td class="paramtype"><a class="el" href="../../dc/d84/group__core__basic.html#ga353a9de602fe76c709e12074a6f362ba">InputArray</a> </td>
          <td class="paramname"><em>src</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype"><a class="el" href="../../dc/d84/group__core__basic.html#gaad17fda1d0f0d1ee069aebb1df2913c0">OutputArray</a> </td>
          <td class="paramname"><em>dst</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">int </td>
          <td class="paramname"><em>thinningType</em> = <code><a class="el" href="../../d9/d29/namespacecv_1_1ximgproc.html#aa244a73deb4e58ae70ee96afe9d2460bacbe8f65466c9d11a295d1bc7413e3404">THINNING_ZHANGSUEN</a></code> </td>
        </tr>
        <tr>
          <td></td>
          <td>)</td>
          <td></td><td></td>
        </tr>
      </table><table class="python_language"><tr><th colspan="999" style="text-align:left">Python:</th></tr><tr><td style="width: 20px;"></td><td>dst</td><td>=</td><td>cv.ximgproc.thinning(</td><td class="paramname">src[, dst[, thinningType]]</td><td>)</td></tr></table>
</div><div class="memdoc">
<p><code>#include &lt;<a class="el" href="../../df/d6c/ximgproc_8hpp.html">opencv2/ximgproc.hpp</a>&gt;</code></p>
<p>Applies a binary blob thinning operation, to achieve a skeletization of the input image. </p>
<p>The function transforms a binary blob image into a skeletized form using the technique of Zhang-Suen.</p>
<dl class="params"><dt>Parameters</dt><dd>
  <table class="params">
    <tr><td class="paramname">src</td><td>Source 8-bit single-channel image, containing binary blobs, with blobs having 255 pixel values. </td></tr>
    <tr><td class="paramname">dst</td><td>Destination image of the same size and the same type as src. The function can work in-place. </td></tr>
    <tr><td class="paramname">thinningType</td><td>Value that defines which thinning algorithm should be used. See <a class="el" href="../../d9/d29/namespacecv_1_1ximgproc.html#aa244a73deb4e58ae70ee96afe9d2460b">cv::ximgproc::ThinningTypes</a> </td></tr>
  </table>
  </dd>
</dl>
</div>
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